What's Wrong with the Current Audit Risk Model?*/QU'EST-CE QUI NE VA PAS DANS LE MODÈLE ACTUEL DE RISQUE DE VÉRIFICATION?

2007 ◽  
Vol 6 (4) ◽  
pp. 343-367 ◽  
Author(s):  
Wally Smieliauskas
Keyword(s):  
Risk Model ◽  
Audit Risk ◽  
Audit Risk Model

A Risk Model to Opine on Internal Control

2010 ◽  
Vol 24 (1) ◽  
pp. 65-78 ◽  
Author(s):  
Abraham D. Akresh
Keyword(s):  
Conceptual Framework ◽  
Internal Control ◽  
Risk Model ◽  
Financial Statements ◽  
Financial Statement ◽  
Material Weakness ◽  
Audit Risk ◽  
Audit Risk Model ◽  
Auditing Standards

SYNOPSIS: In recent years, auditors have reported on the effectiveness of internal control, usually as part of integrated audits. The audit risk model currently in auditing standards was designed for financial statement audits, not internal control audits—a key part of integrated audits. Because the audit of processes (internal control) is conceptually different from the audit of outputs (financial statements), the auditor needs a different risk model to provide a conceptual framework for internal control audits. The model I propose1 provides the auditor a method to determine the appropriate nature, timing, and extent of testing in an integrated audit. My model is focused on the risk of material weakness, rather than the risk of material misstatement. I also show how the auditor would use two different models in an integrated audit.

scholarly journals Risicoanalyse in auditing

2004 ◽  
Vol 78 (9) ◽  
pp. 403-411
Author(s):  
H. H. W. Kloosterman
Keyword(s):  
Risk Model ◽  
Audit Risk ◽  
Audit Risk Model

Prof. Dr. K.Y. Mollema heeft mij in het MAB van december 2003 en januari/februari van 2004 aangenaam verrast (Mollema, 2003, 2004). Het is goed dat hij de discussie over risicoanalyse in de accountantscontrole weer heeft opgepakt. Hij biedt in deel twee van het artikel een stappenplan aan om aan de hand van scorecards de risicoanalyse uit te voeren. Na lezing had ik een dubbel gevoel. Enerzijds vind ik de analyse die Mollema wil laten maken om de onderneming in kaart te brengen heel waardevol. Anderzijds vind ik dat die analyse niet leidt tot risicoanalyse in de accountantscontrole. Als ik dat vind, moet ik dat wel toelichten. Ik geef daarom eerst een analyse van de artikelen van Mollema op basis van zijn hoofdlijnen van kritiek op het Audit Risk Model (ARM). Tijdens de behandeling van de opmerkingen en stellingen van Mollema breek ik een lans voor een Bayesiaans model voor de risicoanalyse in auditing. Bij mijn behandeling van het laatste kritiekpunt van Mollema ga ik proberen het verschil tussen ‘Insurance’ en ‘Assurance’ weer te geven. Ik sluit het artikel af met een belofte en een samenvatting.

scholarly journals Determinants of audit risk assessment for governmental audits in Indonesia: a study of the national audit board of the republic of Indonesia

2015 ◽  
Vol 13 (1) ◽  
pp. 379-388
Author(s):  
Agung Nur Probohudono ◽  
Payamta ◽  
Sri Hantoro
Keyword(s):  
Risk Assessment ◽  
Risk Model ◽  
National Audit ◽  
Audit Risk ◽  
Factors Affecting ◽  
And Topology ◽  
Audit Risk Model ◽  
The Republic ◽  
Expertise Level

This study aimed to determine the influence of: geography, demography and topology; culture; maturity of organization (age of government); maturity of people; auditor’s capability in the assigned region; expertise / education level; and experience of auditing team in risk assessment; on the examination of audit risk by The National Audit Board of The Republic of Indonesia (Badan Pemeriksa Keuangan (BPK) in Indonesia. This study found the factors affecting the audit risk model in general. This study identified several factors that influence the determination of audit risk assessment which occur when conducting local governmental audits in Indonesia. This study was conducted by identifying the factors that might influence the risk of audit used by The National Audit Board. The results of the identification are elaborated in some of the items included in the questionnaire. The number of respondents in this study was 143 respondents as Auditors of The National Audit Board in Indonesia. This study conducted multiple regression analysis. Maturity of people, auditor’s capability, and expertise level have a significant influence on the risk assessment. These factors are derived from an auditor’s judgment when they perform the examination seen from the condition of local government in Indonesia

The Audit Risk Model: An Empirical Test for Conditional Dependencies among Assessed Component Risks

2000 ◽  
Vol 19 (2) ◽  
pp. 105-117 ◽  
Author(s):  
Richard B. Dusenbury ◽  
Jane L. Reimers ◽  
Stephen W. Wheeler
Keyword(s):  
Risk Model ◽  
Empirical Test ◽  
Explanatory Power ◽  
Risk Assessments ◽  
Theoretical Research ◽  
Future Research ◽  
Linear Modeling ◽  
Audit Risk ◽  
Modeling Process ◽  
Audit Risk Model

Professional standards and prior theoretical research indicate that assessed audit risk components should be conditionally dependent. In an experiment, experienced auditors made the risk assessments that are, in practice, inputs for using the audit risk model for planning the extent of detailed testing. Conditional dependencies were tested using a sequential linear modeling process that added the previously assessed risk components to the model (e.g., inherent risk assessments added to predict subsequent control risk assessments) as the last independent variable. Results showed that the previously assessed risk substantially increased the explanatory power of the models in accounting for variation in the subsequently assessed components. The results support the notion that audit risk components are assessed conditionally. Thus, they provide a defense for practitioners' claims that they are appropriately using the model and give guidance to future research on the audit risk model.

Attribute Sampling: A Belief-Function Approach to Statistical Audit Evidence

2000 ◽  
Vol 19 (1) ◽  
pp. 145-155 ◽  
Author(s):  
Peter R. Gillett ◽  
Rajendra P. Srivastava
Keyword(s):  
Sample Size ◽  
Risk Model ◽  
Financial Statements ◽  
Belief Function ◽  
Occurrence Rate ◽  
Evidence Based ◽  
Belief Functions ◽  
Audit Risk ◽  
Audit Risk Model ◽  
Audit Evidence

The Dempster-Shafer belief function framework has been used to model the aggregation of audit evidence based on subjectively assessed beliefs. This paper shows how statistical evidence obtained by means of attribute sampling may be represented as belief functions, so that it can be incorporated into such models. In particular, the article shows: (1) how to determine the sample size in attribute sampling to obtain a desired level of belief that the true attribute occurrence rate of the population lies in a given interval; (2) what level of belief is obtained for a specified interval, given the sample result. As intuitively expected, we find that the sample size increases as the desired level of belief in the interval increases. In evaluating the sample results, our findings are again intuitively appealing. For example, provided the sample occurrence rate falls in the interval B for a given number of occurrences of the attribute, we find that the belief in B, Bel(B), increases as the sample size increases. However, if the sample occurrence rate falls outside of the interval, then Bel(B) is zero. Note that, in general, both Bel(B) and Bel(notB) are zero when the sample occurrence rate falls at the end points of the interval. These results extend similar results already available for variables sampling. However, the auditor faces an additional problem for attribute sampling: how to convert belief in an interval for control exceptions into belief in an interval for material misstatements in the financial statements, so that it can be combined with evidence from other sources in implementations of the Audit Risk Model.

Are Audit Program Plans Risk-Adjusted?

1999 ◽  
Vol 18 (1) ◽  
pp. 55-74 ◽  
Author(s):  
Theodore J. Mock ◽  
Arnold M. Wright
Keyword(s):  
Risk Model ◽  
Experimental Studies ◽  
Strong Relationship ◽  
Audit Risk ◽  
Accounts Receivable ◽  
Inherent Risk ◽  
Audit Risk Model ◽  
Broad Array ◽  
Audit Program ◽  
And Training

Prior archival and experimental studies provide conflicting results regarding the extent to which audit program plans are responsive to client risks, as prescribed by the Audit Risk Model. The purpose of this study is to corroborate and extend archival research on this issue by considering a broader set of client risks and incorporating a number of methodological improvements. Data were gathered on risk assessments and evidential plans in the accounts receivable area from the working papers of 74 randomly selected manufacturing clients (42 general manufacturing and 32 high-technology manufacturing). The results indicate a statistical association between the level of and changes in a limited number of assessed client risks (e.g., management aggressiveness and the inherent risk of an existence misstatement) and evidential plans. In addition, audit programs were found to change little over time with many tests done across a broad array of engagements. Overall, the responsiveness of evidential plans to risks, although limited, was found to be greater in the present study than prior research. These results, which generally replicate prior research, indicate the lack of a strong relationship between client risks and audit programs and thus raise a number of important questions for audit theory, practice and training.

Explaining the conditional nature of the audit risk model

1989 ◽  
Vol 7 (1) ◽  
pp. 107-114 ◽  
Author(s):  
James A. Yardley
Keyword(s):  
Risk Model ◽  
Audit Risk ◽  
Audit Risk Model

Group Audits, Group-Level Controls, and Component Materiality: How Much Auditing Is Enough?

2012 ◽  
Vol 88 (2) ◽  
pp. 707-737 ◽  
Author(s):  
Trevor R. Stewart ◽  
William R. Kinney
Keyword(s):  
Risk Model ◽  
Level Structure ◽  
Optimal Solution ◽  
Data Availability ◽  
Group Level ◽  
Component Level ◽  
Audit Risk ◽  
Multiple Components ◽  
Theoretical Support ◽  
Audit Risk Model

ABSTRACT Auditing standards now mandate that group auditors determine and implement appropriate component materiality amounts, which ultimately affect group audit scope, reliability, and value. However, standards are silent about how these amounts should be determined and methods being used in practice vary widely, lack theoretical support, and may either fail to meet the audit objective or do so at excessive cost. We develop a Bayesian group audit model that generalizes and extends the single-component audit risk model to aggregate assurance across multiple components. The model formally incorporates group auditor knowledge of group-level structure, controls, and context as well as component-level constraints imposed by statutory audit or other requirements. Application of the model yields component materiality amounts that achieve the group auditor's overall assurance objective by finding the optimal solution on an efficient materiality frontier. Numerical results suggest group-level controls and structured subgroups of components are central to efficient group audits. Data Availability: Upon request, Dr. Stewart will provide Excel-based software that facilitates exploration and application of the model described in this paper.

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